Development of an acoustic emission monitoring system for crack detection during arc welding

Condition monitoring techniques are employed to monitor the structural integrity of a structure or the performance of a process. They are used to evaluate the structural integrity including damage initiation and propagation in engineering components. Early damage detection, maintenance and repairs can prevent structural failures, reduce maintenance and replacement costs, and guarantee that the structure runs securely during its service life. Acoustic emission (AE) technology is one of the condition monitoring methods widely employed in the industry. AE is an attractive option for condition monitoring purposes, the number of industrial applications where is used is rising. AE signals are elastic stress waves created by the fast release of energy from local sources occurring inside of materials, e.g. crack initiating and propagating. The AE technique includes recording this phenomenon with piezoelectric sensors, which is mounted on the surface of a structure. The signals are subsequently analysed in order to extract useful information about the nature of the AE source. AE has a high sensitivity to crack propagation and able to locate AE activity sources. It is a passive approach. It listens to the elastic stress waves releasing from material and able to operate in real-time monitoring to detect both cracks initiating and propagating. In this study, the use of AE technology to detect and monitor the possible occurrence of cracking during the arc welding process has been investigated. Real-time monitoring of the automated welding operation can help increase productivity and reliability while reducing cost. Monitoring of welding processes using AE technology remains a challenge, especially in the field of real-time data analysis, since a large amount of data is generated during monitoring. Also, during the welding process, many interferences can occur, causing difficulties in the identifications of the signals related to cracking events. A significant issue in the practical use of the AE technique is the existence of independent sources of a signal other than those related to cracking. These spurious AE signals make the discovering of the signals from the cracking activity difficult. Therefore, it is essential to discriminate the signal to identify the signal source. The need for practical data analysis is related to the three main objectives of monitoring, which is where this study has focused on. Firstly, the assessment of the noise levels and the characteristics of the signal from different materials and processes, secondly, the identification of signals arising from cracking and thirdly, the study of the feasibility of online monitoring using the AE features acquired in the initial study. Experimental work was carried out under controlled laboratory conditions for the acquisition of AE signals during arc welding processing. AE signals have been used for the assessment of noise levels as well as to identify the characteristics of the signals arising from different materials and processes. The features of the AE signals arising from cracking and other possible signal sources from the welding process and environment have also collected under laboratory conditions and analysed. In addition to the above mentioned aspects of the study, two novel signal processing methods based on signal correlation have been developed for efficiently evaluating data acquired from AE sensors. The major contributions of this research can be summarised as follows. The study of noise levels and filtering of different arc welding processes and materials is one of the areas where the original contribution is identified with respect to current knowledge. Another key contribution of the present study is the developing of a model for achieving source discrimination. The crack-related signals and other signals arising from the background are compared with each other. Two methods that have the potential to be used in a real-time monitoring system have been considered based on cross-correlation and pattern recognition. The present thesis has contributed to the improvement of the effectiveness of the AE technique for the detection of the possible occurrence of cracking during arc welding

Data Acquisition and Data Analysis in Skin Measurements

The water in skin, particularly in stratum corneum, the outmost skin layer, is very important. However, to measure it is very difficult. OTTER (Opto-thermal transient emission radiometry), AquaFlux, and Epsilon are three novel technologies specifically developed by our research group for such measurements. This thesis describes the latest development of the technologies. The main focus is to develop a state of the art OTTER data acquisition and data analysis software programme based on Pico Technology. The new software programme offers a range of benefits, such as faster sampling rate, better ADC resolutions, more user friendly interfaces, and more functions and features. The modularized dynamic link library based approach, also means it is easier to maintain, update and expand. With the new OTTER software programme, a multiple wavelength detection is carried out, and an enhanced segmented least squares (SLS) fitting is proposed. The results show that by combining multiple wavelength detection and enhanced SLS fitting, OTTER is capable to detect different types of skin damage, and the presence of topically applied solvents as well as depth distribution of solvents within skin. The second main focus is skin characterization by using AquaFlux and Epsilon, as well as other skin measurement instruments, such as Corneometer, Moisture Checker, Hydratest Beauty Pro, and ProscopeHR2 digital microscope. The aims are to have better understanding on the instrument performances, the correlations between instruments, as well as skin damage assessments, and in vitro and in vivo skin solvent penetration. The results show that the combination of AquaFlux and Epsilon can be very useful for skin characterizations, and the ratio of skin hydration and TEWL can be a better index for skin barrier function. The results also show that the Epsilon capacitive occlusion curves can be potentially used for skin damage assessments, as it can detect both the scale of the damage, and the type of the damage. The skin solvent penetration results show that Epsilon can be effectively used for measuring different types of solvents, and a method to quantify solvent concentration in skin has been developed

Quantifying the damage of in-service rolling stock wheelsets using remote condition monitoring

The global railway network is set to continue to expand in terms of size, passenger numbers and freight tonnage in the coming decades. The occurrence of derailments can lead to major network disruption, significant financial losses, damage to infrastructure and rolling stock assets, environmental damage, and possibly fatalities and injuries. Defects in rolling stock wheelsets can potentially result in severe derailments if left to grow to a critical level. Rolling stock wheelsets are maintained using preventative maintenance techniques. Predictive maintenance solutions prevent unexpected failure, boost operational efficiency, and lower costs. The railway industry has been looking into the development of advanced and effective condition monitoring with a low capital cost for the online and real-time assessment of the rolling stock wheels' structural integrity and subcomponents (wheels, bearings, brakes and suspension). Existing wayside measurement systems are based on different technologies, including hot boxes, acoustic arrays, wheel impact load detectors, etc. However, significant flaws, especially bearing failures, are challenging to identify. Hot boxes can only detect bad bearings after they overheat. This indicates that the bearing has failed and will be seized soon. The combination of acoustic emission (AE) and vibration analysis has been used in this study to identify wheelset defects, particularly in wheels and axle bearings. Based on the new approach and thanks to the capability of early fault detection, predictive maintenance methods can be effectively applied whilst minimising the risk of catastrophic failure and reducing the level of disruption to an absolute minimum. The present study looked into the quantitative evaluation of damage in axle bearings using an advanced customised vibroacoustic remote condition monitoring system developed at the University of Birmingham to improve the early fault detectability in in-service rolling stock wheelsets and improve maintenance planning. Laboratory tests using AE sensors and accelerometers were conducted to compare the sensitivity of each technique and evaluate the synergy in combining them. An experiment using the Amsler machine and bearing test rig proved that raw data and Fast Fourier transform (FFT) are inefficient for defect detection. More advanced signal processing techniques, including Kurtosis, were also applied to find the ideal core frequency and bandwidth for a band-pass filter. Cepstral analysis determines the complex natural logarithm of data's Fourier transform, and the power spectrum's inverse Fourier transform. It helps identify the bearing defect's harmonics from vibration measurement. High-frequency harmonics arising from wheel and axle bearing faults were proven to be detectable from the acquired AE signals. The trial at Bescot yard demonstrates wayside measurement using a compact data acquisition system. Kurtogram-based band-pass filters eliminate environmental and undesired vibrations. The filtered signal with a better signal-to-noise ratio has less noise than the original signal. Another real-world wayside measurement was conducted at the Cropredy site to demonstrate train and wheelset defect detection

Advanced signal processing methods for condition monitoring

Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.Web of Scienc

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Over recent years, due to the increase in the epileptic patient population, issues of diagnosing and treatment of epilepsy have become more and more prominent and much research has been done in this field in consequence. However, there are still many gaps and lack of knowledge in interpreting Electroencephalograph (EEG) signals in order to solve the problem. Particular problems in this area include difficulties in detecting the seizure events (due to the different seizure types and their variability from patient to patient or even in an individual over time), and dealing with long-term EEG recordings, which is an onerous and time consuming task for electroencephalographers. The thesis discusses the two problem areas using EEG data from four subjects with overall 21 hours of recording from the CHB-MIT scalp benchmark EEG dataset. We propose a patient specific seizure detection technique, which selects the optimal feature subsets, and train a dedicated classifier for each patient in order to maximize the classification performance. To exploit the characteristics of a patient’s EEG pattern as much as possible, we used a large set of features in the proposed framework, namely time domain, frequency domain, time-frequency domain and nonlinear features, and selected the most crucial features among them by using Conditional Mutual Information Maximization (CMIM) technique. We further performed extensive comparative evaluations against 6 other feature selection methods to demonstrate the superiority of the CMIM. Support Vector Machine (SVM) with the linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set, i.e. an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average sensitivity and specificity rates of 93.78% and 99.05% are obtained using CMIM, respectively

Computer-aided diagnosis of gynaecological abnormality using B-mode ultrasound images

Ultrasound scan is one of the most reliable imaging for detecting/diagnosing of gynaecological abnormalities. Ultrasound imaging is widely used during pregnancy and has become central in the management of the problems of early pregnancy, particularly in miscarriage diagnosis. Also ultrasound is considered as the most important imaging modality in the evaluation of different types of ovarian tumours. The early detection of ovarian carcinoma and miscarriage continues to be a challenging task. It mostly relies on manual examination, interpretation by gynaecologists, of the ultrasound scan images that may use morphology features extracted from the region of interest. Diagnosis depends on using certain scoring systems that have been devised over a long time. The manual diagnostic process involves multiple subjective decisions, with increased inter- and intra-observer variations which may lead to serious errors and health implications. This thesis is devoted to developing computer-based tools that use ultrasound scan images for automatic classification of Ovarian Tumours (Benign or Malignant) and automatic detection of Miscarriage cases at early stages of pregnancy. Our intended computational tools are meant to help gynaecologists to improve accuracy of their diagnostic decisions, while serving as a tool for training radiology students/trainees on diagnosing gynaecological abnormalities. Ultimately, it is hoped that the developed techniques can be integrated into a specialised gynaecology Decision Support System. Our approach is to deal with this problem by adopting a standard image-based pattern recognition research framework that involve the extraction of appropriate feature vector modelling of the investigated tumours, select appropriate classifiers, and test the performance of such schemes using sufficiently large and relevant datasets of ultrasound scan images. We aim to complement the automation of certain parameters that gynaecologist experts and radiologists manually determine, by image-content information attributes that may not be directly accessible without advanced image transformations. This is motivated by, and benefit from, advances in computer vision that led the emergence of a variety of image processing/analysis techniques together with recent advances in data mining and machine learning technologies. An expert observer makes a diagnostic decision with a level of certainty, and if not entirely certain about their diagnostic decisions then often other experts’ opinions are sought and may be essential for diagnosing difficult “Inconclusive cases”. Here we define a quantitative measure of confidence in decisions made by automatic diagnostic schemes, independent of accuracy of decision. In the rest of the thesis, we report on the development of a variety of innovative diagnostic schemes and demonstrate their performances using extensive experimental work. The following is a summary of the main contributions made in this thesis. 1. Using a combination of spatial domain filters and operations as pre-processing procedures to enhance ultrasound images for both applications, namely miscarriage identification and ovarian tumour diagnosis. We show that the Non-local means filter is effective in reducing speckle noise from ultrasound images, and together with other filters we succeed in enhancing the inner border of malignant tumours and reliably segmenting the gestational sac. 2. Developing reliable automated procedures to extract several types of features to model gestational sac dimensional measurements, few of which are manually determined by radiologist and used by gynaecologists to identify miscarriage cases. We demonstrate that the corresponding automatic diagnostic schemes yield excellent accuracy when classified by the k-Nearest Neighbours. 3. Developing several local as well as global image-texture based features in the spatial as well as the frequency domains. The spatial domain features include the local versions of image histograms, first order statistical features and versions of local binary patterns. From the frequency domain, we propose a novel set of Fast Fourier Geometrical Features that encapsulates the image texture information that depends on all image pixel values. We demonstrate that each of these features define Ovarian Tumour diagnostic scheme that have relatively high power of discriminating Benign from Malignant tumours when classified by Support Vector Machine. We show that the Fast Fourier Geometrical Features are the best performing scheme achieving more than 85% accuracy. 4. Introducing a simple measure of confidence to quantify the goodness of the automatic diagnostic decision, regardless of decision accuracy, to emulate real life medical diagnostics. Experimental work in this theis demonstrate a strong link between this measure and accuracy rate, so that low level of confidence could raise an alarm. 5. Conducting sufficiently intensive investigations of fusion models of multi-feature schemes at different level. We show that feature level fusion yields degraded performance compared to all its single components, while score level fusion results in improved results and decision level fusion of three sets of features using majority rule is slightly less successful. Using the measure of confidence is useful in resolving conflicts when two sets of features are fused at the decision level. This leads to the emergence of a Not Sure decision which is common in medical practice. Considering the Not Sure label is a good practice and an incentive to conduct more tests, rather than misclassification, which leads to significantly improved accuracy. The thesis concludes with an intensive discussion on future work that would go beyond improving performance of the developed scheme to deal with the corresponding multi-class diagnostics essential for a comprehensive gynaecology Decision Support System tool as the ultimate goal

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference